LICENSE PLATE CHARACTER RECOGNITION USING MIXTURE OF EXPERT ARCHITECTURE

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NEJATI MANSOUR; POOR GHASEM HOSSEIN

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Journal: ELECTRONIC INDUSTRIES Year:2012 | Volume:3 | Issue:2 (9) Page(s): 41-60

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Information Journal Paper

Title

LICENSE PLATE CHARACTER RECOGNITION USING MIXTURE OF EXPERT ARCHITECTURE

Author(s)

NEJATI MANSOUR | POOR GHASEM HOSSEIN | Issue Writer Certificate

Keywords

LICENSE PLATE RECOGNITIONQ3

MIXTURE OF EXPERTSQ2

MULTILAYER PERCEPTRONQ3

Abstract

This paper presents a new classification framework for Iranian license plate character recognition. In this framework, a set of robust features are calculated from license plate characters based on directional projections, kirsch edge detector and local means. The characters are then classified using MIXTURE OF EXPERTS which use the MULTILAYER PERCEPTRONs (MLPs) as expert and gating networks. The proposed recognition algorithm is evaluated on a database of Iranian license plate characters consisting of 14256 binary images, and the recognition rate of 99.42% is achieved. The proposed algorithm yields better performance of the Iranian license plate character recognition in comparison with conventional methods which use a single MLP neural network.

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APA: Copy

NEJATI, MANSOUR, & POOR GHASEM, HOSSEIN. (2012). LICENSE PLATE CHARACTER RECOGNITION USING MIXTURE OF EXPERT ARCHITECTURE. ELECTRONIC INDUSTRIES, 3(2 (9)), 41-60. SID. https://sid.ir/paper/229591/en

Vancouver: Copy

NEJATI MANSOUR, POOR GHASEM HOSSEIN. LICENSE PLATE CHARACTER RECOGNITION USING MIXTURE OF EXPERT ARCHITECTURE. ELECTRONIC INDUSTRIES[Internet]. 2012;3(2 (9)):41-60. Available from: https://sid.ir/paper/229591/en

IEEE: Copy

MANSOUR NEJATI, and HOSSEIN POOR GHASEM, “LICENSE PLATE CHARACTER RECOGNITION USING MIXTURE OF EXPERT ARCHITECTURE,” ELECTRONIC INDUSTRIES, vol. 3, no. 2 (9), pp. 41–60, 2012, [Online]. Available: https://sid.ir/paper/229591/en

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